6. An introduction to full waveform inversion

Virieux, Jean; Asnaashari, A.; Brossier, Romain; Métivier, Ludovic; Ribodetti, A.; Zhou, Wei

doi:10.1190/1.9781560803027.entry6

Cited by 122 publications

(109 citation statements)

References 143 publications

Supporting

Mentioning

109

Contrasting

Order By: Relevance

“…The theoretical foundations of FWI methods are developed by Tarantola (1984Tarantola ( , 1986 and rep resent the general solution for solving the inver sion of recorded seismic data under an elastic assumption. The first implementations are for an acoustic model (Lailly, 1983;Gauthier et al, 1986;Pica et al, 1990), followed by the elastic isotropic formulation (Mora, 1987;Virieux and Operto, 2009;Operto et al, 2013;Virieux et al, 2017).…”

Section: Full Waveform Seismic Inversionmentioning

confidence: 99%

7. Seismic, rock physics, spatial models, and their integration in reservoir geophysics

Bosch

Mukerji²,

González

2014

Encyclopedia of Exploration Geophysics

View full text Add to dashboard Cite

seismic inversion and rock physics models. Furthermore well logs allow for local validation of the estimations and constraint for the struc ture and properties. Combining the spatially dis tributed information provided by seismic surveys with the localized information obtained from well logging tools requires handling disparate property support scales and use of spatial statisti cal models. Most common models are based on the spatial homogeneity of the mean (one point statistics) and the spatial covariance (two point statistics). Cokriging estimation and multivariate Gaussian simulation are examples of these meth ods, which are very useful in the spatial combina tion of information. In addition, methods based on multipoint statistics are used to model com plex morphology. The integration of the overall data, information, geophysical, and geological knowledge into a reliable reservoir model is elab orated via diverse workflows. A basic sequence includes seismic interpretation, seismic inver sion, reservoir characterization, geostatistical es timation, geostatistical simulation, and dynamic fluid flow modeling. It is common to cycle within the sequence to update the model as additional information about the reservoir arrives. Some workflows proceed in a stepwise manner, while others, with increasing computational require ments, employ joint inversion formulations where multiproperty models are estimated to jointly satisfy the complete set of available data.

show abstract

Section: Full Waveform Seismic Inversionmentioning

confidence: 99%

7. Seismic, rock physics, spatial models, and their integration in reservoir geophysics

Bosch

Mukerji²,

González

2014

Encyclopedia of Exploration Geophysics

View full text Add to dashboard Cite

show abstract

“…Unlike conventional full waveform inversion schemes (see, e.g., Virieux et al . ; Kim et al . ), JMI parameterizes an acoustic subsurface model using both a reflectivity model and a velocity model, which are completely decoupled in the framework of JMI.…”

Section: Introductionmentioning

confidence: 95%

Research note: derivations of gradients in angle‐independent joint migration inversion

Sun¹,

Verschuur

2019

Geophysical Prospecting

View full text Add to dashboard Cite

Although joint migration inversion has been proposed for several years, a thorough derivation and description of the involved gradients was not published. In this paper, we derive the gradient of both the angle‐independent reflectivity and the velocity in a framework of acoustic angle‐independent joint migration inversion. With some further approximations taken, the conclusions shown in previous publications can also be reached from our new derivation.

show abstract

“…In this linearized framework, a challenging source of non linearity is the so-called cycle skipping pathology. When FWI is solved with a classical least-squares difference-based distance [5], [6], cycle skipping drives FWI to spurious local minima as soon as the initial parameter model does not allow to match Hossein S. Aghamiry is with the Institute of Geophysics, University of Tehran, Tehran, Iran, and University Cote d'Azur -CNRS -IRD -OCA, Geoazur, Valbonne, France, e-mail: h.aghamiry@ut.ac.ir, aghamiry@geoazur.unice.fr.…”

Section: Introductionmentioning

confidence: 99%

Compound Regularization of Full-Waveform Inversion for Imaging Piecewise Media

Aghamiry

Gholami

Operto

2020

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

The nonlinear and ill-posed nature of full waveform inversion (FWI) requires us to use sophisticated regularization techniques to solve it. In most applications, the model parameters may be described by physical properties (e.g., wave speeds, density, attenuation, anisotropic parameters) which are piecewise functions of space. Compound regularizations are thus necessary to reconstruct properly such parameters by FWI. We consider different implementations of compound regularizations in the wavefield reconstruction inversion (WRI) method, a formulation of FWI that extends its search space and prevent the so-called cycle skipping pathology. Our hybrid regularizations rely on Tikhonov and total variation (TV) functionals, from which we build two classes of hybrid regularizers: the first class is simply obtained by a convex combination (CC) of the two functionals, while the second relies on their infimal convolution (IC). In the former class, the model of parameters is required to simultaneously satisfy different priors, while in the latter the model is broken into its basic components, each satisfying a distinct prior (e.g. smooth, piecewise constant, piecewise linear). We implement these types of compound regularizations in the WRI optimization problem using the alternating direction method of multipliers (ADMM). Then, we assess our regularized WRI in the framework of seismic imaging applications. Using a wide range of subsurface models, we conclude that compound regularizer based on IC leads to the lowest error in the parameter reconstruction compared to that obtained with the CC counterpart and the Tikhonov and TV regularizers when used independently.

show abstract

6. An introduction to full waveform inversion

Cited by 122 publications

References 143 publications

7. Seismic, rock physics, spatial models, and their integration in reservoir geophysics

7. Seismic, rock physics, spatial models, and their integration in reservoir geophysics

Research note: derivations of gradients in angle‐independent joint migration inversion

Compound Regularization of Full-Waveform Inversion for Imaging Piecewise Media

Contact Info

Product

Resources

About